Ranking with decision tree
- 463 Downloads
Ranking problems have recently become an important research topic in the joint field of machine learning and information retrieval. This paper presented a new splitting rule that introduces a metric, i.e., an impurity measure, to construct decision trees for ranking tasks. We provided a theoretical basis and some intuitive explanations for the splitting rule. Our approach is also meaningful to collaborative filtering in the sense of dealing with categorical data and selecting relative features. Some experiments were made to illustrate our ranking approach, whose results showed that our algorithm outperforms both perceptron-based ranking and the classification tree algorithms in term of accuracy as well as speed.
KeywordsMachine learning Ranking Decision tree Splitting rule
Unable to display preview. Download preview PDF.
- 1.Buntine W, Niblett T (1992) A further comparison of splitting rules for decision-tree induction. Mach Learn 8: 75–85Google Scholar
- 2.Burges C, Shaked T, Renshaw E et al. (2005) Learning to ranking using gradient descent. In: Proceedings of the 22nd international conference on maching learning (ICML-2005), Bonn, Germany, pp 89–96Google Scholar
- 5.Crammer K, Singer Y (2002) Pranking with ranking. Advances in Neural Information Processing Systems 14. MIT Press, Cambridge, pp 641–647Google Scholar
- 10.Herbrich R, Graepel T, Obermayer K (2000) Large margin rank boundaries for ordinal regression. Advance in large margin classifiers. MIT Press, Cambridge, MA. pp 115–132Google Scholar
- 11.Harrington EF (2003) Online ranking/collaborative filtering using the perceptron algorithm. In: Proceedings of the twentieth international conference on machine learning (ICML-2003), Washington, DCGoogle Scholar
- 13.Mitchell TM (1997) Machine Learning. The McGraw-Hill New York, pp 52–78Google Scholar
- 14.Quinlan JR (1986) Induction of decision trees. Machine Learning 1: 81–106Google Scholar
- 15.Quinlan JR (1993) C4.5: programs for Machine learning. Kaufmann, MorganGoogle Scholar
- 19.Shaw WM, Wood JB, Wood RE et al (1991) The cystic fibrosis database: content and research opportunities. LISR 13: 347–366Google Scholar
- 20.Shashua A, Levin A (2003) Ranking with large margin principle: two approaches. In: Proceedings of the conference on Neural information processing systems, (NIPS) 14Google Scholar